The complexity of today's highly competitive industries requires that products be manufactured in as short a period as possible, as well as in a cost efficient manner. Product managers and manufacturing managers must implement a system of designing, developing, and production to bring a product to the market for the least cost, within proper time frame, while sustaining product quality. Further complicating, the market constantly shifts, with the introduction of “new and improved” items, causing some products to be viable for only six months to one year. Thus product and manufacturing managers need sufficient information to implement the most efficient system of operation.
Many manufacturing industries employ statistical methodology, such as statistical process control, to control the manufacturing process. In statistical process control (SPC), processes are controlled from the data collected from samples drawn at certain intervals and studied for the behavior of process. SPC is used in the manufacturing process to asses if the process is capable and predictable. In order to assess the process an abundance of data is necessary, making SPC a data driven process. Yet, before manufactures can apply SPC, a prototype must be made first. This prototype then goes through a series of quality and reality tests. Data is collected during the tests to modify the engineering drawing idea to strengthen the product or process. Once the manufacturing process begins inspection and collection of data is done to study the health of the process by conducting SPC.
The application of SPC begins with the collection of samples and measure of features. An X-bar and R-chart is plotted to see if the process causes the parts to fall with in the control limits. If the values fall with in the limit, then the products are considered statistically acceptable. If the values fall beyond the limit, then the cause of the variation is investigated and corrected so that the process yields the desired output. This kind of trial and error method to control the process has proven to be good, provided the volume of production is high, however it requires reliable data and controlling of all variables in a manufacturing process, as well as performing tests on prototypes. Thus this kind of process control method becomes an expensive and time-consuming task.
The very nature of SPC is one of trial-and-error guesswork with an inherent deficiency of it's own, known as margin of error. There are many variables to manipulate, causing a mass amount of time to be devoted to fine tuning the process of a product to determine the correct process step or strength of the process. Methods employing this trial-and-error form of SPC are not proactive process control techniques, meaning they are after the fact or reactive process control techniques. This aspect of SPC, along with the large costs associated with creating and testing prototypes, is undesirable to manufacturing industries where time and resources are limited. The manufacturing environment changes over time, which makes the expensive development costs of process models employing this form of SPC not practical and unrealistic. Manufacturing industries are seeking for a proactive process control technique that is not expensive, yet very effective.
Therefore, there is a need within the manufacturing industry for a methodology that makes the manufacturing process proactive rather than a reactive process by predicting the capability of a process without having sample data, thus eliminating the cost of application of traditional SPC and need to impose control on key characteristics. Such a technique will be cost saving, time saving, produce a prime quality product, and higher product yield.